Genome-wide analysis of the GLP gene family and overexpression of GLP1-5-1 to promote lignin accumulation during early somatic embryo development in Dimocarpus longan.

Longan (Dimocarpus longan Lour.) is an economically important subtropical fruit tree. Its fruit quality and yield are affected by embryo development. As a plant seed germination marker gene, the germin-like protein (GLP) gene plays an important role in embryo development. However, the mechanism underlying the role of the GLP gene in somatic embryos is still unclear. Therefore, we conducted genome-wide identification of the longan GLP (DlGLP) gene and preliminarily verified the function of DlGLP1-5-1. Thirty-five genes were identified as longan GLP genes and divided into 8 subfamilies. Based on transcriptome data and qRT‒PCR results, DlGLP genes exhibited the highest expression levels in the root, and the expression of most DlGLPs was upregulated during the early somatic embryogenesis (SE) in longan and responded to high temperature stress and 2,4-D treatment; eight DlGLP genes were upregulated under MeJA treatment, and four of them were downregulated under ABA treatment. Subcellular localization showed that DlGLP5-8-2 and DlGLP1-5-1 were located in the cytoplasm and extracellular stroma/chloroplast, respectively. Overexpression of DIGLP1-5-1 in the globular embryos (GEs) of longan promoted the accumulation of lignin and decreased the H2O2 content by regulating the activities of ROS-related enzymes. The results provide a reference for the functional analysis of DlGLPs and related research on improving lignin accumulation in the agricultural industry through genetic engineering.

scGraph: a graph neural network-based approach to automatically identify cell types.

MOTIVATION: Single-cell technologies play a crucial role in revolutionizing biological research over the past decade, which strengthens our understanding in cell differentiation, development and regulation from a single-cell level perspective. Single-cell RNA sequencing (scRNA-seq) is one of the most common single cell technologies, which enables probing transcriptional states in thousands of cells in one experiment. Identification of cell types from scRNA-seq measurements is a fundamental and crucial question to answer. Most previous studies directly take gene expression as input while ignoring the comprehensive gene-gene interactions. RESULTS: We propose scGraph, an automatic cell identification algorithm leveraging gene interaction relationships to enhance the performance of the cell-type identification. scGraph is based on a graph neural network to aggregate the information of interacting genes. In a series of experiments, we demonstrate that scGraph is accurate and outperforms eight comparison methods in the task of cell-type identification. Moreover, scGraph automatically learns the gene interaction relationships from biological data and the pathway enrichment analysis shows consistent findings with previous analysis, providing insights on the analysis of regulatory mechanism. AVAILABILITY AND IMPLEMENTATION: scGraph is freely available at https://github.com/QijinYin/scGraph and https://figshare.com/articles/software/scGraph/17157743. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.